link to paper:

arxiv.org/abs/2505.15059

We implement these oracles using heat-kernel truncation & Varadhan's asymptotics, linking our method to entropy-regularized proximal point method on Wasserstein spaces, in the latter case.

Joint work with Yunrui Guan and @shiqianma.bsky.social

Our bounds show how key factors—like the number of matches and treatment balance—impact Gaussian approximation accuracy.

We also introduce multiplier bootstrap bounds for obtaining finite-sample valid, data-driven confidence intervals.

Matching-based ATE estimators align treated and control units to estimate causal effects without strong parametric assumptions.

Using Malliavin-Stein method we establish Gaussian Approximation bounds for these estimators.

thanks, resent the email now!

How well RF performs in these settings? That’s still an open question.

Bottom-line: Time to compare SGD-trained NNs with RF and not kernel methods!

Going beyond mean-field regime for SGD trained NNs certainly helps. Recent works connect learnability of SGD trained NNs with leap complexity and information exponent of function classes (like single and multi index models) with the goal of explaining feature learning.

It also creates an intriguing parallel with NNs: greedy-trained partitioning models and SGD-trained NNs (in the mean-field regime) both thrive under specific structural assumptions (eg. MSP) but struggle otherwise.

However, under MSP, greedy RFs are provably better that SGD-trained 2-NNs!

In our work:

arxiv.org/abs/2411.04394

we show that If the true regression function satisfies MSP, greedy training works well with 𝑂(log 𝑑) samples.

Otherwise, it struggles.

This settles the question of learnability for greedy recursive partitioning algorithms like CART.

MSP is used to argue that SGD trained 2-layer NNs are better than vanilla kernel methods.

But how do neural nets compare with random forest (RF) trained using greedy algorithms like CART?

add me please
🙋

Yes, but is the cover indicative of RL notations by any chance :P